Overview

Dataset statistics

Number of variables18
Number of observations3652
Missing cells597
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory513.7 KiB
Average record size in memory144.0 B

Variable types

NUM13
CAT3
BOOL2

Warnings

Temperature_Max is highly correlated with Temperature_Midday and 2 other fieldsHigh correlation
Temperature_Midday is highly correlated with Temperature_Max and 2 other fieldsHigh correlation
Temperature_Min is highly correlated with Temperature_Midday and 2 other fieldsHigh correlation
Temperature_Evening is highly correlated with Temperature_Midday and 2 other fieldsHigh correlation
Special_Event is highly correlated with HolidayHigh correlation
Holiday is highly correlated with Special_EventHigh correlation
Snow_5Days has 565 (15.5%) missing values Missing
Day is uniformly distributed Uniform
Sunshine_Percentage has 807 (22.1%) zeros Zeros
Snow_5Days has 2793 (76.5%) zeros Zeros
Temperature_Deviation has 43 (1.2%) zeros Zeros
Precipiation_5Days has 511 (14.0%) zeros Zeros
Precipiation has 1876 (51.4%) zeros Zeros

Reproduction

Analysis started2020-09-21 11:19:44.267315
Analysis finished2020-09-21 11:20:14.190022
Duration29.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Passengers
Real number (ℝ≥0)

Distinct3045
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6387.8023
Minimum0
Maximum34878
Zeros33
Zeros (%)0.9%
Memory size28.5 KiB
2020-09-21T13:20:14.315181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile825
Q11547
median4029
Q39771
95-th percentile18593.95
Maximum34878
Range34878
Interquartile range (IQR)8224

Descriptive statistics

Standard deviation6033.488385
Coefficient of variation (CV)0.9445327362
Kurtosis0.9687784571
Mean6387.8023
Median Absolute Deviation (MAD)2909
Skewness1.236183308
Sum23328254
Variance36402982.09
MonotocityNot monotonic
2020-09-21T13:20:14.451010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0330.9%
 
101050.1%
 
152550.1%
 
162550.1%
 
124150.1%
 
105740.1%
 
93940.1%
 
162340.1%
 
90240.1%
 
141840.1%
 
Other values (3035)357998.0%
 
ValueCountFrequency (%) 
0330.9%
 
241< 0.1%
 
541< 0.1%
 
5231< 0.1%
 
5441< 0.1%
 
ValueCountFrequency (%) 
348781< 0.1%
 
313261< 0.1%
 
305491< 0.1%
 
304151< 0.1%
 
301741< 0.1%
 

Revision
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
0
3617 
1
 
35
ValueCountFrequency (%) 
0361799.0%
 
1351.0%
 
2020-09-21T13:20:14.554959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Temperature_Midday
Real number (ℝ)

HIGH CORRELATION

Distinct383
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.84942497
Minimum-11.2
Maximum32.7
Zeros6
Zeros (%)0.2%
Memory size28.5 KiB
2020-09-21T13:20:14.645353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-11.2
5-th percentile-0.9
Q16
median13.3
Q319.5
95-th percentile26.4
Maximum32.7
Range43.9
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.509758988
Coefficient of variation (CV)0.6622676895
Kurtosis-0.8275978816
Mean12.84942497
Median Absolute Deviation (MAD)6.7
Skewness-0.04654652102
Sum46926.1
Variance72.41599803
MonotocityNot monotonic
2020-09-21T13:20:14.780219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10.4230.6%
 
19.5230.6%
 
19220.6%
 
18.5220.6%
 
19.2210.6%
 
16.7210.6%
 
10.2210.6%
 
15.4210.6%
 
16.9210.6%
 
14.3210.6%
 
Other values (373)343694.1%
 
ValueCountFrequency (%) 
-11.21< 0.1%
 
-111< 0.1%
 
-10.81< 0.1%
 
-101< 0.1%
 
-9.61< 0.1%
 
ValueCountFrequency (%) 
32.71< 0.1%
 
32.520.1%
 
32.31< 0.1%
 
32.21< 0.1%
 
3220.1%
 

Sunshine_Percentage
Real number (ℝ≥0)

ZEROS

Distinct103
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.03216053
Minimum0
Maximum101
Zeros807
Zeros (%)22.1%
Memory size28.5 KiB
2020-09-21T13:20:14.918567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median28
Q368
95-th percentile98
Maximum101
Range101
Interquartile range (IQR)66.25

Descriptive statistics

Standard deviation34.98270198
Coefficient of variation (CV)0.9446573324
Kurtosis-1.221677876
Mean37.03216053
Median Absolute Deviation (MAD)28
Skewness0.4849864348
Sum135241.4502
Variance1223.789438
MonotocityNot monotonic
2020-09-21T13:20:15.054751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
080722.1%
 
11062.9%
 
100892.4%
 
99792.2%
 
3641.8%
 
2551.5%
 
98481.3%
 
97461.3%
 
14451.2%
 
4431.2%
 
Other values (93)227062.2%
 
ValueCountFrequency (%) 
080722.1%
 
11062.9%
 
2551.5%
 
3641.8%
 
4431.2%
 
ValueCountFrequency (%) 
10120.1%
 
100892.4%
 
99792.2%
 
98481.3%
 
97461.3%
 

Snow_5Days
Real number (ℝ≥0)

MISSING
ZEROS

Distinct32
Distinct (%)1.0%
Missing565
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean0.7884677681
Minimum0
Maximum36
Zeros2793
Zeros (%)76.5%
Memory size28.5 KiB
2020-09-21T13:20:15.180503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.379960264
Coefficient of variation (CV)4.286745002
Kurtosis42.86467956
Mean0.7884677681
Median Absolute Deviation (MAD)0
Skewness6.047057175
Sum2434
Variance11.42413138
MonotocityNot monotonic
2020-09-21T13:20:15.295652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%) 
0279376.5%
 
2371.0%
 
5361.0%
 
3260.7%
 
1250.7%
 
4240.7%
 
10190.5%
 
6180.5%
 
9170.5%
 
8150.4%
 
Other values (22)772.1%
 
(Missing)56515.5%
 
ValueCountFrequency (%) 
0279376.5%
 
1250.7%
 
2371.0%
 
3260.7%
 
4240.7%
 
ValueCountFrequency (%) 
361< 0.1%
 
3520.1%
 
3420.1%
 
3220.1%
 
3040.1%
 

Temperature_Deviation
Real number (ℝ)

ZEROS

Distinct188
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.153313253
Minimum-12
Maximum14.6
Zeros43
Zeros (%)1.2%
Memory size28.5 KiB
2020-09-21T13:20:15.424715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-4.345
Q1-1.1
median1.3
Q33.5
95-th percentile6.5
Maximum14.6
Range26.6
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation3.352714264
Coefficient of variation (CV)2.907028299
Kurtosis0.0291209036
Mean1.153313253
Median Absolute Deviation (MAD)2.3
Skewness-0.1429645275
Sum4211.9
Variance11.24069294
MonotocityNot monotonic
2020-09-21T13:20:15.575056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2531.5%
 
1.6511.4%
 
2.1491.3%
 
3.3491.3%
 
3491.3%
 
1.1481.3%
 
2.6471.3%
 
3.4461.3%
 
2.9451.2%
 
0.2451.2%
 
Other values (178)317086.8%
 
ValueCountFrequency (%) 
-121< 0.1%
 
-11.71< 0.1%
 
-11.61< 0.1%
 
-11.31< 0.1%
 
-10.820.1%
 
ValueCountFrequency (%) 
14.620.1%
 
11.31< 0.1%
 
11.220.1%
 
111< 0.1%
 
10.920.1%
 

Temperature_Max
Real number (ℝ)

HIGH CORRELATION

Distinct391
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.61771632
Minimum-9.3
Maximum34.9
Zeros4
Zeros (%)0.1%
Memory size28.5 KiB
2020-09-21T13:20:15.712387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-9.3
5-th percentile0.2
Q17.5
median15.1
Q321.5
95-th percentile28.3
Maximum34.9
Range44.2
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.801185208
Coefficient of variation (CV)0.6020903003
Kurtosis-0.8390175138
Mean14.61771632
Median Absolute Deviation (MAD)6.9
Skewness-0.07894840193
Sum53383.9
Variance77.46086107
MonotocityNot monotonic
2020-09-21T13:20:15.847115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20260.7%
 
21.8230.6%
 
10.4220.6%
 
20.1220.6%
 
18.6210.6%
 
17.3210.6%
 
19.5210.6%
 
11.1210.6%
 
24210.6%
 
24.5210.6%
 
Other values (381)343394.0%
 
ValueCountFrequency (%) 
-9.31< 0.1%
 
-9.11< 0.1%
 
-91< 0.1%
 
-8.71< 0.1%
 
-8.51< 0.1%
 
ValueCountFrequency (%) 
34.91< 0.1%
 
34.81< 0.1%
 
34.61< 0.1%
 
34.520.1%
 
34.31< 0.1%
 

Temperature_Min
Real number (ℝ)

HIGH CORRELATION

Distinct304
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.212568456
Minimum-16.1
Maximum21.5
Zeros15
Zeros (%)0.4%
Memory size28.5 KiB
2020-09-21T13:20:15.990018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-16.1
5-th percentile-4.4
Q10.6
median6.4
Q311.8
95-th percentile16.5
Maximum21.5
Range37.6
Interquartile range (IQR)11.2

Descriptive statistics

Standard deviation6.729163507
Coefficient of variation (CV)1.083153217
Kurtosis-0.8913467671
Mean6.212568456
Median Absolute Deviation (MAD)5.6
Skewness-0.1184410974
Sum22688.3
Variance45.2816415
MonotocityNot monotonic
2020-09-21T13:20:16.126576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10.9320.9%
 
0.5300.8%
 
3.8280.8%
 
8.4270.7%
 
2.5270.7%
 
-0.3260.7%
 
-0.2260.7%
 
0.6250.7%
 
10.3230.6%
 
3230.6%
 
Other values (294)338592.7%
 
ValueCountFrequency (%) 
-16.11< 0.1%
 
-13.81< 0.1%
 
-13.41< 0.1%
 
-12.71< 0.1%
 
-12.620.1%
 
ValueCountFrequency (%) 
21.51< 0.1%
 
20.91< 0.1%
 
20.51< 0.1%
 
20.41< 0.1%
 
19.91< 0.1%
 

Temperature_Evening
Real number (ℝ)

HIGH CORRELATION

Distinct377
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.55603944
Minimum-10.7
Maximum32.7
Zeros13
Zeros (%)0.4%
Memory size28.5 KiB
2020-09-21T13:20:16.273091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-10.7
5-th percentile-1.345
Q14.6
median11.9
Q318.1
95-th percentile24.9
Maximum32.7
Range43.4
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.341949876
Coefficient of variation (CV)0.7218692804
Kurtosis-0.8752155081
Mean11.55603944
Median Absolute Deviation (MAD)6.8
Skewness0.015468861
Sum42202.65604
Variance69.58812773
MonotocityNot monotonic
2020-09-21T13:20:16.407828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16230.6%
 
3.7220.6%
 
3.3220.6%
 
6.7210.6%
 
8.5200.5%
 
17.2200.5%
 
14.1200.5%
 
5.6200.5%
 
15.4200.5%
 
1.1200.5%
 
Other values (367)344494.3%
 
ValueCountFrequency (%) 
-10.71< 0.1%
 
-10.11< 0.1%
 
-9.620.1%
 
-9.51< 0.1%
 
-9.21< 0.1%
 
ValueCountFrequency (%) 
32.71< 0.1%
 
321< 0.1%
 
31.61< 0.1%
 
31.520.1%
 
31.41< 0.1%
 

Precipiation_5Days
Real number (ℝ≥0)

ZEROS

Distinct629
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.77343921
Minimum0
Maximum113.7
Zeros511
Zeros (%)14.0%
Memory size28.5 KiB
2020-09-21T13:20:16.858976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.7
median10.5
Q324.9
95-th percentile54.8
Maximum113.7
Range113.7
Interquartile range (IQR)23.2

Descriptive statistics

Standard deviation19.07172298
Coefficient of variation (CV)1.137019232
Kurtosis3.219842168
Mean16.77343921
Median Absolute Deviation (MAD)10
Skewness1.682689055
Sum61256.6
Variance363.7306173
MonotocityNot monotonic
2020-09-21T13:20:16.999405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
051114.0%
 
0.2661.8%
 
0.1451.2%
 
0.3351.0%
 
0.5260.7%
 
1.4250.7%
 
0.8250.7%
 
0.4240.7%
 
7.1240.7%
 
1.1240.7%
 
Other values (619)284778.0%
 
ValueCountFrequency (%) 
051114.0%
 
0.1451.2%
 
0.2661.8%
 
0.3351.0%
 
0.4240.7%
 
ValueCountFrequency (%) 
113.71< 0.1%
 
113.21< 0.1%
 
1131< 0.1%
 
112.41< 0.1%
 
110.11< 0.1%
 

Precipiation
Real number (ℝ≥0)

ZEROS

Distinct301
Distinct (%)8.3%
Missing13
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean3.347513053
Minimum0
Maximum70.2
Zeros1876
Zeros (%)51.4%
Memory size28.5 KiB
2020-09-21T13:20:17.133311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.1
95-th percentile17.61
Maximum70.2
Range70.2
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation7.172589902
Coefficient of variation (CV)2.142662266
Kurtosis15.86812738
Mean3.347513053
Median Absolute Deviation (MAD)0
Skewness3.465247569
Sum12181.6
Variance51.4460459
MonotocityNot monotonic
2020-09-21T13:20:17.265048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0187651.4%
 
0.11243.4%
 
0.2812.2%
 
0.3601.6%
 
0.5471.3%
 
0.4421.2%
 
0.6351.0%
 
0.8290.8%
 
1.2290.8%
 
0.9290.8%
 
Other values (291)128735.2%
 
ValueCountFrequency (%) 
0187651.4%
 
0.11243.4%
 
0.2812.2%
 
0.3601.6%
 
0.4421.2%
 
ValueCountFrequency (%) 
70.21< 0.1%
 
67.91< 0.1%
 
63.61< 0.1%
 
58.21< 0.1%
 
57.81< 0.1%
 

Wind
Real number (ℝ≥0)

Distinct245
Distinct (%)6.7%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean8.016350124
Minimum2.2
Maximum31.1
Zeros0
Zeros (%)0.0%
Memory size28.5 KiB
2020-09-21T13:20:17.403638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.6
Q14.8
median6.4
Q39.8
95-th percentile17.8
Maximum31.1
Range28.9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.659221494
Coefficient of variation (CV)0.58121482
Kurtosis3.290420095
Mean8.016350124
Median Absolute Deviation (MAD)1.9
Skewness1.738259749
Sum29123.4
Variance21.70834493
MonotocityNot monotonic
2020-09-21T13:20:17.535198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.3852.3%
 
4.8832.3%
 
5.5832.3%
 
5.8742.0%
 
4.7732.0%
 
4.6671.8%
 
6.3671.8%
 
3.8641.8%
 
5.2631.7%
 
4.5621.7%
 
Other values (235)291279.7%
 
ValueCountFrequency (%) 
2.220.1%
 
2.320.1%
 
2.41< 0.1%
 
2.570.2%
 
2.680.2%
 
ValueCountFrequency (%) 
31.11< 0.1%
 
30.51< 0.1%
 
30.31< 0.1%
 
301< 0.1%
 
29.120.1%
 

Holiday
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
0
3502 
1
 
149
6
 
1
ValueCountFrequency (%) 
0350295.9%
 
11494.1%
 
61< 0.1%
 
2020-09-21T13:20:17.675120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-21T13:20:17.753224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:17.834101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Special_Event
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
0
3502 
1
 
150
ValueCountFrequency (%) 
0350295.9%
 
11504.1%
 
2020-09-21T13:20:17.901752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Year
Real number (ℝ≥0)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.499726
Minimum2006
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size28.5 KiB
2020-09-21T13:20:17.968116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12008
median2010.5
Q32013
95-th percentile2015
Maximum2015
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.87229323
Coefficient of variation (CV)0.001428646417
Kurtosis-1.224127228
Mean2010.499726
Median Absolute Deviation (MAD)2.5
Skewness0.0001444845368
Sum7342345
Variance8.250068399
MonotocityIncreasing
2020-09-21T13:20:18.063768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
201236610.0%
 
200836610.0%
 
201536510.0%
 
201336510.0%
 
201136510.0%
 
200936510.0%
 
200736510.0%
 
201436510.0%
 
201036510.0%
 
200636510.0%
 
ValueCountFrequency (%) 
200636510.0%
 
200736510.0%
 
200836610.0%
 
200936510.0%
 
201036510.0%
 
ValueCountFrequency (%) 
201536510.0%
 
201436510.0%
 
201336510.0%
 
201236610.0%
 
201136510.0%
 

Day_in_Month
Real number (ℝ≥0)

Distinct31
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.72782037
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size28.5 KiB
2020-09-21T13:20:18.171657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.800529314
Coefficient of variation (CV)0.5595517437
Kurtosis-1.193846831
Mean15.72782037
Median Absolute Deviation (MAD)8
Skewness0.006914871984
Sum57438
Variance77.4493162
MonotocityNot monotonic
2020-09-21T13:20:18.285667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
11203.3%
 
281203.3%
 
41203.3%
 
61203.3%
 
81203.3%
 
101203.3%
 
121203.3%
 
141203.3%
 
161203.3%
 
181203.3%
 
Other values (21)245267.1%
 
ValueCountFrequency (%) 
11203.3%
 
21203.3%
 
31203.3%
 
41203.3%
 
51203.3%
 
ValueCountFrequency (%) 
31701.9%
 
301103.0%
 
291123.1%
 
281203.3%
 
271203.3%
 

Day
Categorical

UNIFORM

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
Sunday
522 
Monday
522 
Thursday
522 
Wednesday
522 
Tuesday
522 
Other values (2)
1042 
ValueCountFrequency (%) 
Sunday52214.3%
 
Monday52214.3%
 
Thursday52214.3%
 
Wednesday52214.3%
 
Tuesday52214.3%
 
Saturday52114.3%
 
Friday52114.3%
 
2020-09-21T13:20:18.407359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-21T13:20:18.483792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:18.609605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length7.142935378
Min length6

Month
Categorical

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
July
310 
August
310 
March
310 
October
310 
January
310 
Other values (7)
2102 
ValueCountFrequency (%) 
July3108.5%
 
August3108.5%
 
March3108.5%
 
October3108.5%
 
January3108.5%
 
May3108.5%
 
December3108.5%
 
September3008.2%
 
April3008.2%
 
November3008.2%
 
Other values (2)58215.9%
 
2020-09-21T13:20:18.730493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-21T13:20:18.842280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length6.148959474
Min length3

Interactions

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2020-09-21T13:19:53.938560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:54.064244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:54.187618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:19:54.670450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:19:55.836263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:55.944210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.057100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.163152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.267898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.379476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.497676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.607049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.718126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.835630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:56.947660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.057243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.174014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.283640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.397120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.513421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.623532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.732028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.846648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:57.971429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:58.088381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:19:58.693438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:58.809902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:58.935546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:59.060305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:59.179243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:59.295920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:59.423486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:19:59.768620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:59.887412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:19:59.999321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:00.110270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:00.227477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:00.338990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:20:00.677083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:00.786024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:00.901879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:01.016390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:20:01.412870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:01.525526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:20:02.516599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:02.643845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:02.760095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:02.883002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:20:03.245400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:03.370999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:03.487089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:03.608288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:03.732711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:03.851387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:03.968713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.091716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.213264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.329108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.440516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.553928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.663513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.767894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.881881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:04.986247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.094334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.207630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.314383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.420649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.531261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.649916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.758058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.872190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:05.988580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.100313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.208214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.325565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.434022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.544496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.661927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.771257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.880372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:06.993899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.120350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.237087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.357355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.481228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.606078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.722262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.846425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:07.963334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:08.082056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:08.204941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:08.322876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:08.439739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:08.561394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:08.678444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.007193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.125965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.240692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.353258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.461332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-09-21T13:20:09.683347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.795215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:09.909960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.018482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.127600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.242023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.356828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.462878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.570888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.685017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.795226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:10.900026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.013900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.119962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.228909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.342170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.448745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.553499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.663471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.784726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:11.897489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.019893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.140445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.254779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.367764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.489097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.601546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.716106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.837607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:12.951926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:13.064317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-21T13:20:18.956527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-21T13:20:19.220296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-21T13:20:19.461297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-21T13:20:19.706597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-21T13:20:19.921779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-21T13:20:13.334569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:13.685628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:13.905426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-21T13:20:14.024870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

PassengersRevisionTemperature_MiddaySunshine_PercentageSnow_5DaysTemperature_DeviationTemperature_MaxTemperature_MinTemperature_EveningPrecipiation_5DaysPrecipiationWindHolidaySpecial_EventYearDay_in_MonthDayMonth
0258004.952.05.02.35.1-0.80.912.14.69.91120061SundayJanuary
1197302.40.05.02.33.40.62.712.40.36.11120062MondayJanuary
2104402.211.05.01.62.9-1.61.812.40.05.30020063TuesdayJanuary
398000.80.00.00.21.1-1.00.610.30.06.40020064WednesdayJanuary
411390-0.388.00.0-1.80.9-5.4-3.20.30.04.50020065ThursdayJanuary
510570-2.70.00.0-3.2-1.8-6.7-2.90.30.05.10020066FridayJanuary
69140-2.40.00.0-2.7-1.8-3.7-3.20.00.04.40020067SaturdayJanuary
715810-2.80.00.0-2.9-1.9-3.9-2.70.00.04.40020068SundayJanuary
88080-2.70.00.0-2.9-2.3-4.0-2.90.00.04.60020069MondayJanuary
97920-3.50.00.0-3.3-2.7-4.8-3.30.00.03.800200610TuesdayJanuary

Last rows

PassengersRevisionTemperature_MiddaySunshine_PercentageSnow_5DaysTemperature_DeviationTemperature_MaxTemperature_MinTemperature_EveningPrecipiation_5DaysPrecipiationWindHolidaySpecial_EventYearDay_in_MonthDayMonth
3642019.081.00.04.010.11.95.24.60.03.300201522TuesdayDecember
3643019.385.00.03.310.3-0.65.24.60.04.500201523WednesdayDecember
3644018.762.00.03.19.40.55.14.60.02.911201524ThursdayDecember
36450110.2100.00.03.311.30.34.14.60.03.211201525FridayDecember
3646017.6100.00.01.59.0-0.92.50.00.02.611201526SaturdayDecember
3647018.199.00.01.19.3-1.72.30.00.02.900201527SundayDecember
3648011.20.00.0-1.02.4-2.10.40.00.02.700201528MondayDecember
3649011.219.00.00.23.9-2.62.80.00.03.100201529TuesdayDecember
3650012.80.00.01.84.11.82.10.00.04.300201530WednesdayDecember
3651015.10.00.03.05.71.74.89.22.85.361201531ThursdayDecember